NTI.cm {iCAMP}

Calculate nearest taxon index (NTI) under multiple metacommunities

Description

Calculate nearest taxon index (NTI) of each sample with parallel computing. his function can deal with local communities under different metacommunities (regional pools).

Usage

NTI.cm(comm, dis, meta.group = NULL,
       meta.spool = NULL, nworker = 4, memo.size.GB = 50,
       weighted = c(TRUE, FALSE), rand = 1000,
       check.name = TRUE, output.MNTD = c(FALSE, TRUE),
       sig.index = c("SES", "NTI", "Confidence", "RC", "all"),
       silent = FALSE)

Arguments

comm

community data matrix. rownames are sample names. colnames are species names.

dis

Phylogenetic distance matrix.

meta.group

matrix or data.frame, a one-column (n x 1) matrix indicating which metacommunity each sample belongs to. rownames are sample IDs. first column is metacommunity names. Such that different samples can belong to different metacommunities. If input a n x m matrix, only the first column is used. NULL means all samples belong to the same metacommunity. Default is NULL, means all samples from the same metacommunity.

meta.spool

a list object, each element is a character vector listing all taxa IDs in a metacommunity. The names of the elements indicate metacommunity names, which should be the same as the metacommunity names in meta.group. Default is NULL, means to use the observed taxa in comm across samples within the same metacommunity that is defined by meta.group.

nworker

for parallel computing. Either a character vector of host names on which to run the worker copies of R, or a positive integer (in which case that number of copies is run on localhost). default is 4, means 4 threads will be run.

memo.size.GB

numeric, to set the memory size as you need, so that calculation of large tree will not be limited by physical memory. unit is Gb. default is 50Gb.

weighted

Logic, consider abundances or not (just presence/absence). default is TRUE.

rand

integer, randomization times. default is 1000.

check.name

logic, whether to check the taxa names in comm and dis, which must be the same and in the same order; if not match, remove mismatched names and change to the same order. default is TRUE.

output.MNTD

logic, if TRUE, the NTI and MNTD will be output, if FALSE, only output NTI.

sig.index

character, the index for null model significance test. SES or NTI, standard effect size, i.e. nearest taxon index (NTI); Confidence, percentage of null values less extreme than the observed value, i.e. non-parametric one-side confidence level; RC, modified Raup-Crick index (RC) based on MNTD, i.e. count the number of null MNTD lower than observed MNTD plus a half of the number of null MNTD equal to observed MNTD, to get alpha, then calculate MNTD-based RC as (2 x alpha - 1); all, output all the three indexes. default is SES. If input a vector, only the first element will be used.

silent

logic, if FALSE, some messages will show during calculation.

Details

This function is particularly designed for samples from different metacommunities. The null model "taxa shuffle" will be done under different metacommunities, separately (and independently). All other details are the same as the function NTI.p.

Value

If output.MNTD is FALSE, output is a one-column matrix where rownames are sample IDs and the only column shows NTI values. If output.MNTD is TRUE, output is a list of three elements.

NTI

matrix, NTI values.

MNTD

matrix, observed MNTD.

MNTD.rand

array, null MNTD values, the third dimension represent randomization times.

Note

Version 1: 2021.8.4

Author(s)

Daliang Ning

References

Webb CO, Ackerly DD, and Kembel SW. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 18:2098-2100

Kembel, S.W. (2009). Disentangling niche and neutral influences on community assembly: assessing the performance of community phylogenetic structure tests. Ecol Lett, 12, 949-960.

Chase, J.M., Kraft, N.J.B., Smith, K.G., Vellend, M. & Inouye, B.D. (2011). Using null models to disentangle variation in community dissimilarity from variation in alpha-diversity. Ecosphere, 2, 1-11.

Ning, D., Yuan, M., Wu, L., Zhang, Y., Guo, X., Zhou, X. et al. (2020). A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nature Communications, 11, 4717.

See Also

NTI.p

Examples

data("example.data")
comm=example.data$comm
pd=example.data$pd

# in this example, 10 samples from one metacommunity,
# the other 10 samples from another metacommunity.
meta.group=data.frame(meta.com=c(rep("meta1",10),rep("meta2",10)))
rownames(meta.group)=rownames(comm)

nworker=2 # parallel computing thread number.
rand.time=4 # usually use 1000 for real data.
sigmntd=NTI.cm(comm=comm, meta.group=meta.group,
               dis=pd, nworker = nworker,
               weighted = TRUE, rand = rand.time,
               sig.index="all")
NTI=sigmntd$SES
CMNTD=sigmntd$Confidence
RCMNTD=sigmntd$RC